Electrocardiography is a diagnostic procedure for the detection and diagnosis of heart abnormalities. The electrocardiogram (ECG) signal contains important information that is utilized by physicians for the diagnosis and analysis of heart diseases. So good quality ECG signal plays a vital role for the interpretation and identification of pathological, anatomical and physiological aspects of the whole cardiac muscle. However, the ECG signals are corrupted by noise which severely limit the utility of the recorded ECG signal for medical evaluation. The most common noise presents in the ECG signal is the high frequency noise caused by the forces acting on the electrodes.
An ambulatory electrocardiography device, such as a Holter monitor, is a portable device for monitoring electrical activity of the central nervous system for long periods of time. For many years, such devices have been used to record electrocardiogram signals, which contain important information that is used for the diagnosis and analysis of heart diseases and conditions, such as atrial fibrillation (AF). Use of Holter monitors for extended periods of time is essential given the paroxysmal, often short-lived, and frequently asymptomatic nature of AF. Clinically, monitoring for AF is important because, despite often being paroxysmal and associated with minimal or no symptoms, these arrhythmias are often associated with severely adverse health consequences, including stroke and heart failure. Motion and noise (MN) artifacts are significant during Holter recordings and can lead to false detections of AF. Clinicians have cited MN artifacts in ambulatory monitoring devices as the most common cause of false alarms, loss of signal, and inaccurate readings.
Previous computational efforts have largely relied on MN artifact removal, and some of the popular methods include linear filtering, adaptive filtering, wavelet denoising and Bayesian filtering methods. One main disadvantage of the adaptive filtering methods is that they require a reference signal which is presumed to be correlated in some way with the MN artifacts. The wavelet denoising approach attempts to separate clean and noisy wavelet coefficients, but this approach can be difficult since it requires determination of thresholds. Bayesian filtering requires estimation of optimal parameters using any variant of Kalman filtering methods: extended Kalman filter (EKF), extended Kalman smoother (EKS) and unscented Kalman filter (UKF).
While the above-mentioned signal processing approaches have been applied, they fail to satisfactorily solve problems associated with MN artifacts, and consequently MN artifacts remain a key obstacle to accurate detection of AF and atrial flutter, which is an equally problematic arrhythmia.
Accordingly, there is a need for methods and systems that can separate clean ECG portions from segments with MN artifacts in real time for more accurate diagnosis and treatment of clinically important atrial arrhythmias.